Advantages of Sensor Fusion With GPS in ADAS Vehicles

When I think of the Global Position System (GPS), or any global navigation satellite system (GNSS), I don’t associate it with, “cutting-edge technology.” Especially not when it’s connected with autonomous vehicles or cars with advanced driver assistance systems (ADAS). GPS has been in use for years now, and while it’s quite useful for showing us where to go around town, it hasn’t been considered suitable for any kind of self-driving applications. Sensors like LIDAR and radar are now at the forefront of next-generation cars. Despite its flaws, I think GNSS has a significant place on the vehicles of the future, particularly when combined with other sensors currently in use. Techniques like precise point positioning (PPP), inertial measurement, and real-time kinematics (RTK) can overcome the shortfalls of systems like GPS and make it an important component of the connected car ecosystem.

Pros and Cons of GNSS

Every sensor out there comes with some advantages and disadvantages. That’s why so many ADAS enabled vehicles use multi-sensor fusion, to overcome the shortfalls of some sensors with others. If you use a sensor without considering its strengths and weaknesses your system end up somewhere it’s not supposed to be. When it comes to GNSS there are a lot of positives that are tempered by a few very important negatives.

Pros

Simple - In general GPS is relatively simple. It’s been fully available for civilian use for nearly 20 years now, which means we’re quite familiar with it by now. Other emerging technologies that are being used in ADAS cars, like LIDAR and machine learning, are a little more difficult to use. When it comes to integrating multiple technologies into a single sensing ecosystem simplicity is valuable.

Inexpensive - The long commercial lifetime of GNSS has helped it in this area as well. It’s been around the block and has been common enough for companies to invest lots of money into developing inexpensive versions of this technology. Researchers are still working on making GPS type sensors even more economical. Smart cars are already expensive, so their sensors need to be fairly cheap in order to make them viable.

Separated - When it comes to autonomous vehicles most of the systems we’re talking about are fully integrated. This becomes a problem when you need to check the veracity of your incoming data. You can’t make your car into Noah’s Ark and have two of each sensor, just to double check the system. GNSS determines position based on the movement of satellites, not on a car’s immediate surroundings. This level of separation makes it a great choice for data authentication.

GPS has been helping cars navigate for years.

Cons

Inaccurate - This is probably the largest problem when it comes to GPS. The common type of GNSS receiver will only be able to accurately determine its location within a few meters. That level of inaccuracy is simply not acceptable for primary navigation on a vehicle. There are a few fixes to this problem, namely RTK and PPP, but they also have their own shortfalls which I’ll discuss later.

Unreliable - In order for a GNSS receiver to work as exactly as we need it to, it needs line of sight to at least 4 satellites. If I’m out driving on the open road, that’s no problem. If I’m in a city that has lots of tall buildings, though, suddenly I lose connection to the satellites and I could be in trouble. This problem makes it difficult to use GNSS as the primary guidance system for a vehicle. Using an inertial measurement unit (IMU) can help with this problem, but they have enough error to make them an imperfect solution.

Requires Mapping - GPS is only half of its own puzzle, the other part being a map. My car not only needs to know its exact position, it needs to compare that to the exact location of the road I’m driving on. This isn’t such a big problem, especially since Google has been mapping the world, but it’s still a concern.

GNSS can ensure your car doesn’t accidentally break the rules of the road.

Incorporating GNSS Into ADAS Vehicles

As I said earlier every sensor in the ecosystem of connected cars brings its own strengths and weaknesses. These are overcome by “fusing” sensor data together, either before or after processing, so that we can more accurately sense an environment. GNSS’s unique features make it ideal for a supervisory role in a smart car.

First, let’s talk a little more about RTK and PPP. These two schemes make GNSS accurate down to the centimeter, which is what we need for self-driving cars. RTK uses a base station and a moving receiver to get down to this level of accuracy. PPP uses a dual band receiver and some complex math to determine a more exact location. They both have shortcomings: RTK requires significant infrastructure that may or may not be used in the long term and PPP takes up to 10 minutes to find a location. If companies want to invest in the base stations required for RTK, it would go a long way towards making GPS more viable. However, I’m going to assume they won’t and will talk about integrating a PPP system into your vehicle.

It’s absolutely imperative that your system has supervisory functions. Your software needs to be checking itself for things like memory errors and needs to ensure that all of its sensors are operating correctly. With an accurate map to check against, PPP could provide that kind of oversight. The nice thing is that checking may not need to be done for every decision, but only every once in awhile. If that’s the case in your system, PPP’s processing time may not be a problem. It could determine exact position and speed every few minutes, and check that against what the primary sensors are saying. If there’s a discrepancy you’ll then know there’s a problem and can shut down the car before things get out of hand.

Vehicles with ADAS features are becoming more common, but are still quite complex. The standalone nature of GPS and its ease of operation make it attractive for a connected car. With the right arrangement you could use GNSS to verify the decisions of your primary system, providing oversight at low monetary and complexity costs.